CN105913093A - Template matching method for character recognizing and processing - Google Patents

Template matching method for character recognizing and processing Download PDF

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CN105913093A
CN105913093A CN201610284102.XA CN201610284102A CN105913093A CN 105913093 A CN105913093 A CN 105913093A CN 201610284102 A CN201610284102 A CN 201610284102A CN 105913093 A CN105913093 A CN 105913093A
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template
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CN105913093B (en
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梅亚敏
于鸿洋
张平
曾创
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University of Electronic Science and Technology of China
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/243—Aligning, centring, orientation detection or correction of the image by compensating for image skew or non-uniform image deformations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition

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Abstract

The present invention discloses a template matching method for character recognizing and processing, belonging to the technical field of pattern recognition and image processing. The method comprises the following steps: preprocessing the characteristics extraction of an inputted template and a to-be-recognized image; performing rectangle frame calibration by using adjacent communicable regions in the image and word region as units, and obtaining a plurality of characteristic rectangles; and then performing multiple times of normalizations of coordinate origins and scales of the current template image and the image to be recognized; calculating the similarity of the characteristic set for each normalization; and taking the largest similarity of characteristic set as the matching similarity of the current template image; and finally obtaining the template with the maximum matching similarity of all the template images that is taken as the best matching template of the current image to be recognized. The character recognizing module of the invention is used for character recognizing and processing, especially for a medical examination sheet recognition system, which can overcome the influences of factors such as scene image wrinkling, scale change and light change on character recognition, thus improving the character recognition rate.

Description

A kind of template matching method processed for Text region
Technical field
The invention belongs to pattern recognition and technical field of image processing, the template matching being specifically related in character recognition technology Technology.
Background technology
Along with the high speed development of the progress of information technology, particularly artificial intelligence technology, OCR (optical character recognition) technology Having been achieved for a lot of achievement in information identification field, also step into the practical stage of socialization simultaneously, OCR technique is successfully applied to In society's all trades and professions, also there is universal application in medical industry.
From the viewpoint of statistical-simulation spectrometry, the problem identifying an actually pattern classification of character.Both at home and abroad Scholar proposes many different identifying schemes, is broadly divided into recognition methods based on grader and side based on template matching Method.
The method utilizing grader to be identified can obtain preferable recognition result, but the method for grader needs big The learning sample of amount is trained.Template matching algorithm is typically to mate bianry image, is to realize discrete input pattern One of effective way of classification, essence is certain similarity between tolerance input pattern and sample, and taking similarity the maximum is Input pattern generic.It, according to the visual pattern extraction feature of character, is identified by relevant matches principle.
Angularly considering from real-time, algorithm complexity, template matching algorithm disclosure satisfy that the task of character recognition.But Template matching also has its defect, i.e. quick in yardstick polytropy and the illumination polytropy of different scene images to same class character Sense, easily produces coupling deviation.
Summary of the invention
The technical problem to be solved is to provide a kind of template matching method processed for Text region, the party Method carries out mating optimizing, to determine the Optimum Matching template of image to be identified (test sample) in template set.
The template matching method processed for Text region of the present invention, comprises the following steps:
Image to be identified and several template images (quantity of template image is configured based on practical application request) are entered Row Image semantic classification, described Image semantic classification includes image slant correction, image denoising, image gray processing, image binaryzation；
Image to be identified and each template image are carried out feature extraction, obtain characteristic of correspondence set:
To the character area of each template image in units of adjacent connected domain, carry out the demarcation of rectangle frame, obtain template The characteristic set of imageWhereinRepresent that the lateral coordinates of jth feature square in current template image T, longitudinal coordinate, pixels across are long Degree, longitudinal length in pixels, j={1,2,3 ..., n}, n represent the number of characteristic rectangle in template image T；
To the character area of image to be identified in units of adjacent connected domain, carry out the demarcation of rectangle frame, obtain waiting to know The characteristic set of other imageWhereinRepresent the lateral coordinates of ith feature rectangle, longitudinal coordinate, horizontal picture in current image S to be identified Element length, longitudinal length in pixels, i={1,2,3 ..., m}, m represent the number of characteristic rectangle in image S to be identified；
Template image and image to be identified carry out repeatedly zero normalized, dimension normalization processes, and often enters Normalized of row, then calculate a characteristic set similarity, takes maximum characteristic set similarity as current template figure The matching similarity of picture；Take the template image corresponding to maximum match similarity and correctly mate mould as current image to be identified Plate；Wherein, the computing formula of characteristic set similarity sim is:αi∩βjRepresent current figure to be identified The rectangular area overlapping area size of the ith feature rectangle of picture and the jth characteristic rectangle of current template image, αi∪βiTable Show the rectangular area union area of the ith feature rectangle of front image to be identified and the jth characteristic rectangle of current template image Size.
Further, in order to reduce computation complexity, when dimension normalization processes, first scaling is set to R also Carry out a dimension normalization to process, then obtain optimal scaling based on climbing algorithm screening and carry out the yardstick normalizing of correspondence Change processes, wherein R=ws/wT, parameter wsFor the picture traverse of current image to be identified, wTHorizontal image for current template image Width；Or R=hs/hT, parameter hsFor the picture traverse of current image to be identified, hTHorizontal figure image width for current template image Degree.
In sum, owing to have employed technique scheme, the invention has the beneficial effects as follows: solve the pleat of scene image The impact that Text region is caused by the factors such as wrinkle, yardstick is changeable and illumination is changeable, is effectively improved character identification rate.
Accompanying drawing explanation
Fig. 1 be the specific embodiment of the invention realize block diagram.
Fig. 2 be the present invention a kind of normalization anchor point and Normalized Scale choose coordinate diagram.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with embodiment and accompanying drawing, to this Bright it is described in further detail.
The template matching method of the present invention being used for Text region process, it is as follows that it implements step:
S1: Image semantic classification process:
S1-1: input image to be identified.Image the most to be identified can be by online picture pick-up device collection, it is also possible to be The scene image preserved.In present embodiment, the image pattern of collection utilizes camera to shoot, and image pattern is from 13 kinds Different types of papery medicalization verification certificate.For same category of same laboratory test report, when taking pictures by choose different angle, away from From and illumination, with the test sample storehouse of abundant image to be identified.
S1-2: image slant correction: first detect the angle of inclination of image, then use according to the angle of inclination detected Radiation conversion carries out rotation correction to image, i.e. can get satisfactory image.
S1-3: image denoising: in this detailed description of the invention, uses bilateral Filter bilateral filtering to carry out image Denoising, in conjunction with pixel value similarity and a kind of compromise of spatial neighbor degree of image, considers grey similarity simultaneously, permissible Retain edge well.
S1-4: image gray processing: the picture of collected by camera is 24 true coloured pictures, needs to be converted into gray-scale map.Ash is represented with g Gray value after degreeization, R, G, B represent the red, green, blue component in true coloured picture, g=0.299R+0.587G+0.114B respectively.
S1-5: image binaryzation: character picture binaryzation is usually and the gray-scale map of 256 colors is converted into only black-and-white two color Binary map.By selecting suitable threshold value T, each pixel in scanogram and threshold value T compare the most one by one Relatively, i.e.Wherein (x, y) represents the gray value after gray proces to f, and (x y) represents f ' The pixel value of image after binaryzation, thr is threshold value.In this detailed description of the invention, carry out selected threshold by local threshold.
S2: template matching process:
S2-1: template image (hereinafter referred to as template) and the feature extraction of image to be identified (test sample): choose wherein One template, by the character area of template in units of adjacent connected domain, carries out the demarcation of rectangle frame, obtains the feature of template Set:
T = { ( x 0 T , y 0 T , w 0 T , h 0 T ) , ... , ( x j T , y j T , w j T , h j T ) , ... , ( x n T , y n T , w n T , h n T ) }
Wherein,Represent the lateral coordinates of jth feature square, longitudinal coordinate, horizontal picture in template T Element length, longitudinal length in pixels, j={1,2,3 ..., n}；N represents the number of characteristic rectangle in this template T.
In like manner, the sample to be tested after pretreatment is carried out feature extraction, obtains the characteristic set of test sample:
S = { ( x 0 S , y 0 S , w 0 S , h 0 S ) , ... , ( x i S , y i S , w i S , h i S ) , ... , ( x m S , y m S , w m S , h m S ) }
Wherein,Represent the lateral coordinates of ith feature rectangle in test sample S, longitudinal coordinate, Pixels across length, longitudinal length in pixels, i={1,2,3 ..., m}；M represents the number of characteristic rectangle in current test sample S.
After obtaining the set feature of template T and sample S, the two is carried out zero normalization and dimension normalization.Return The characteristic set of the template T ' after one change is respectively as follows:
T ′ = { ( x 0 ′ T , y 0 ′ T , w 0 ′ T , h 0 ′ T ) , ... , ( x j ′ T , y j ′ T , w j ′ T , h j ′ T ) , ... , ( x n ′ T , y n ′ T , w n ′ T , h n ′ T ) }
Wherein,Represent the template T ' middle ith feature rectangle after normalization lateral coordinates, Longitudinal coordinate, pixels across length, longitudinal length in pixels, n represents the number of this template middle characteristic rectangle of T '.Obtain normalizing simultaneously The characteristic set of the sample S ' after change is:
S ′ = { ( x 0 ′ S , y 0 ′ S , w 0 ′ S , h 0 ′ S ) , ... , ( x i ′ S , y i ′ S , w i ′ S , h i ′ S ) , ... , ( x n ′ S , y n ′ S , w n ′ S , h n ′ S ) }
Wherein,Represent the sample S ' middle ith feature rectangle after normalization lateral coordinates, Longitudinal coordinate, pixels across length, longitudinal length in pixels, m represents the number of the current middle characteristic rectangle of test sample S '.
After obtaining the set feature of template T ' and sample S ', by asking characteristic set similarity sim between T ' and S ' Whether judgment sample mates with template.In theory, if the value of sim is 1, then it represents that mate completely between sample with template.Ask The formula of characteristic set similarity is:Wherein, αi∩βjRepresent that the i-th of current image to be identified is special Levy the rectangular area overlapping area size of the jth characteristic rectangle of rectangle and current template image, αi∪βiRepresent the most to be identified The rectangular area union size of the ith feature rectangle of image and the jth characteristic rectangle of current template image.
When there is overlap the rectangular area of ith feature rectangle and jth characteristic rectangle, αi∩βjAnd αi∪βiCalculating Method is as follows:
α i ∩ β j = [ min ( x i ′ S + w i ′ S , x j ′ T + w j ′ T ) - max ( x i ′ S , x j ′ T ) ] * [ min ( y i ′ S + h i ′ S , y j ′ T + h j ′ T ) - max ( y i ′ S , y i ′ S ) ]
α i ∪ β j = w i ′ S * h i ′ S + w j ′ T * h j ′ T - α i ∩ β j
When the rectangular area of ith feature rectangle and jth characteristic rectangle does not has overlap, αi∩βjAnd αi∪βiMeter Calculation method is as follows:
αi∩βj=0
α i ∪ β j = w i ′ S * h i ′ S + w j ′ T * h j ′ T
The traversal normalization of S2-2: anchor point and size: current just in the template of comparison in order to test sample is normalized to, Then must obtain anchor point and best scale normalization ratio accurately.Anchor point i.e. needs the reference point of normalized zero, Template and test sample need to choose identical rectangular characteristic as anchor point, just can carry out correct zero normalization.
According to priori, each rectangular characteristic of laboratory test report template is known, chooses current template here One of them feature specified, as anchor point, then searches for the anchor point of correspondence in test sample.
See Fig. 2, different anchor points and different scalings and constitute a coordinate system.In a coordinate system, by different Anchor point and different scalings travel through.When choosing different anchor points and scaling, the calculating according to sim is public respectively Formula carries out characteristic of correspondence set Similarity Measure, obtains the characteristic set similarity set after all traversals, and therefrom chooses The maximum, as the matching similarity of current template, is designated as SIM.
Before traversal anchor point, can first carry out Preliminary screening, reduce traversal scope.Known R is optimal scaling value Big probable value, before traversal scaling, first scaling is fixed tentatively as R, then utilizes climbing algorithm to carry out screening and obtain Optimal scaling (optimum shown in Fig. 2).Wherein R=ws/wTOr R=hs/hT, parameter wsImage for current test sample Width, wTHorizontal picture traverse for current template；Parameter hsFor the picture traverse of current test sample, hTHorizontal stroke for current template Picture traverse.
S2-3 template COLLECTION TRAVERSALSThe: current test sample is compared successively with 13 templates.When comparison, according to Step in S2-2, by the anchor point of the anchor point of test sample and dimension normalization to template and yardstick, finally gives 13 coupling phases Set like degree: { SIM1,SIM2,...,SIM13, take the template corresponding to the maximum in 13 matching similarities as working as The Optimum Matching template of front test sample.
S3: Text region exports:
S3-1 image text positions: the space of a whole page feature of 13 laboratory test report templates is carried out typing, in laboratory test report template Each project carries out String localization, method during location, each project and character area to be identified being used picture rectangle frame Demarcate, each project is numbered simultaneously.According to the step in S2, determining the corresponding optimum mould of test sample After plate, i.e. can get the space of a whole page feature of test sample.Now, the rectangle frame in template is positioned Information application to test sample In, the locking of rectangle frame uses self-adapted search method to be accurately positioned.
S3-2 character classification identification: after image text positions successfully, knows carrying out classification according to projects different in template Not.The result identified shows or passes through printer output result or the field according to system application by computer display This FIELD Data is incorporated in system, obtains whole relevant informations of this character.

Claims (2)

1. the template matching method processed for Text region, it is characterised in that comprise the following steps:
Image to be identified and several template images are carried out Image semantic classification, described Image semantic classification include image slant correction, Image denoising, image gray processing, image binaryzation；
Image to be identified and each template image are carried out feature extraction, obtain characteristic of correspondence set:
To the character area of each template image in units of adjacent connected domain, carry out the demarcation of rectangle frame, obtain template image Characteristic setWhereinRepresent that the lateral coordinates of jth feature square in current template image T, longitudinal coordinate, pixels across are long Degree, longitudinal length in pixels, j={1,2,3 ..., n}, n represent the number of characteristic rectangle in template image T；
To the character area of image to be identified in units of adjacent connected domain, carry out the demarcation of rectangle frame, obtain figure to be identified The characteristic set of picture WhereinRepresent the lateral coordinates of ith feature rectangle, longitudinal coordinate, horizontal stroke in current image S to be identified To length in pixels, longitudinal length in pixels, i={1,2,3 ..., m}, m represent the number of characteristic rectangle in image S to be identified；
Template image and image to be identified carry out zero normalized, dimension normalization processes, and the most once returns One change processes, then calculate a characteristic set similarity, takes the maximum characteristic set similarity coupling as current template image Similarity；Take the Optimum Matching template as current image to be identified of the template image corresponding to maximum match similarity；
Wherein, the computing formula of characteristic set similarity sim is:Wherein, αi∩βjRepresent and currently treat Identify the ith feature rectangle of image and the rectangular area overlapping area size of the jth characteristic rectangle of current template image, αi ∪βiRepresent the rectangular area of the ith feature rectangle of front image to be identified and the jth characteristic rectangle of current template image also Collection size.
2. the method for claim 1, it is characterised in that when dimension normalization processes, first scaling is set to R And carry out dimension normalization and process, then obtain optimal scaling based on climbing algorithm screening and carry out the yardstick of correspondence and return One change processes, wherein R=ws/wT, parameter wsFor the picture traverse of current image to be identified, wTHorizontal figure for current template image Image width degree；Or R=hs/hT, parameter hsFor the picture traverse of current image to be identified, hTHorizontal image for current template image Width.
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CN108984578B (en) * 2017-05-31 2022-03-11 株式会社日立制作所 Computer, document identification method and system
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